Precision Agriculture in Latvia in the Last 20 Years

2017 ◽  
Vol 8 (2) ◽  
pp. 698-702
Author(s):  
A. Gailums
Keyword(s):  
Precision Agriculture ◽  
Large Scale ◽  
Precision Farming ◽  
Livestock Farming ◽  
Precision Livestock Farming ◽  
Crop Farming ◽  
Automatic Steering ◽  
Fruit Growing

The establishment of the system Soil – Yield that occured in Latvia during the 1970–80s could be considered as the beginning of precision farming with the available technologies. The first precision farming technologies have been associated with harvesting where combines and tractors with the automatic steering were used. The precision agriculture in Latvia includes various branches. Latvia farmers are using precision crop farming, precision livestock farming, precision fruit growing, precision bee keeping, precision farming greenhouse and precision growing berries. Precision farming technologies in Latvia are introduced mainly in large scale farms, with more than 1000 ha. The most important researches in precision farming in Latvia were done in 2000s.

How remote sensing is offering complementing and diverging opportunities for precision agriculture users and researchers

2017 ◽  
Vol 8 (2) ◽  
pp. 383-387
Author(s):  
R. Jackson ◽  
R. C. Gaynor ◽  
A. Bentley ◽  
J. Hickey ◽  
I. Mackay ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Large Scale ◽  
Agricultural Research ◽  
Yellow Rust ◽  
Precision Farming ◽  
Recent Advances ◽  
Production Agriculture

Precision farming advances are providing opportunities in both production agriculture and agricultural research. For growers and agronomists, the benefits of identifying where crops are stressed, the location of weeds and estimating yields on a large scale are clear. Researchers, who have different needs, can benefit from a detailed focus on a specific characteristic, such as one disease (e.g. yellow rust). This paper will review how recent advances in technology are beginning to allow the development of specialised tools within research and agriculture and how current precision agriculture tools can be effective at measuring desirable traits.

scholarly journals WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

2018 ◽  
Vol 10 (9) ◽  
pp. 1423 ◽  
Author(s):  
Inkyu Sa ◽  
Marija Popović ◽  
Raghav Khanna ◽  
Zetao Chen ◽  
Philipp Lottes ◽  
...  
Keyword(s):  
Neural Network ◽  
Precision Agriculture ◽  
Large Scale ◽  
Deep Neural Network ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Precision Farming ◽  
High Fidelity ◽  
Ground Sample ◽  
Global Consistency

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.

scholarly journals Detection and Analysis of Sow Targets Based on Image Vision

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Kaidong Lei ◽  
Chao Zong ◽  
Ting Yang ◽  
Shanshan Peng ◽  
Pengfei Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Recognition Rate ◽  
Model Parameters ◽  
Livestock Farming ◽  
Data Set ◽  
Attention Model ◽  
Precision Livestock Farming ◽  
Area Distribution ◽  
Complex Scenes ◽  
Detection And Recognition

In large-scale sow production, real-time detection and recognition of sows is a key step towards the application of precision livestock farming techniques. In the pig house, the overlap of railings, floors, and sows usually challenge the accuracy of sow target detection. In this paper, a non-contact machine vision method was used for sow targets perception in complex scenarios, and the number position of sows in the pen could be detected. Two multi-target sow detection and recognition models based on the deep learning algorithms of Mask-RCNN and UNet-Attention were developed, and the model parameters were tuned. A field experiment was carried out. The data-set obtained from the experiment was used for algorithm training and validation. It was found that the Mask-RCNN model showed a higher recognition rate than that of the UNet-Attention model, with a final recognition rate of 96.8% and complete object detection outlines. In the process of image segmentation, the area distribution of sows in the pens was analyzed. The position of the sow’s head in the pen and the pixel area value of the sow segmentation were analyzed. The feeding, drinking, and lying behaviors of the sow have been identified on the basis of image recognition. The results showed that the average daily lying time, standing time, feeding and drinking time of sows were 12.67 h(MSE 1.08), 11.33 h(MSE 1.08), 3.25 h(MSE 0.27) and 0.391 h(MSE 0.10), respectively. The proposed method in this paper could solve the problem of target perception of sows in complex scenes and would be a powerful tool for the recognition of sows.

scholarly journals Exploring Smart Glasses for Augmented Reality: A Valuable and Integrative Tool in Precision Livestock Farming

Animals ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 903 ◽  
Author(s):  
Caria ◽  
Sara ◽  
Todde ◽  
Polese ◽  
Pazzona
Keyword(s):  
Augmented Reality ◽  
Precision Agriculture ◽  
New Technology ◽  
Animal Husbandry ◽  
Battery Life ◽  
Livestock Farming ◽  
Single Issue ◽  
Precision Livestock Farming ◽  
Smart Glasses ◽  
Remote Assistance

The growing interest in Augmented Reality (AR) systems is becoming increasingly evident in all production sectors. However, to the authors’ knowledge, a literature gap has been found with regard to the application of smart glasses for AR in the agriculture and livestock sector. In fact, this technology allows farmers to manage animal husbandry in line with precision agriculture principles. The aim of this study was to evaluate the performances of an AR head-wearable device as a valuable and integrative tool in precision livestock farming. In this study, the GlassUp F4 Smart Glasses (F4SG) for AR were explored. Laboratory and farm tests were performed to evaluate the implementation of this new technology in livestock farms. The results highlighted several advantages of F4SG applications in farm activities. The clear and fast readability of the information related to a single issue, combined with the large number of readings that SG performed, allowed F4SG adoption even in large farms. In addition, the 7 h of battery life and the good quality of audio-video features highlighted their valuable attitude in remote assistance, supporting farmers on the field. Nevertheless, other studies are required to provide more findings for future development of software applications specifically designed for agricultural purposes.

scholarly journals Precision Agriculture for Resource Use Efficiency in Smallholder Farming Systems in Sub-Saharan Africa: A Systematic Review

2021 ◽  
Vol 13 (3) ◽  
pp. 1158
Author(s):  
Cecilia M. Onyango ◽  
Justine M. Nyaga ◽  
Johanna Wetterlind ◽  
Mats Söderström ◽  
Kristin Piikki
Keyword(s):  
Resource Use ◽  
Precision Agriculture ◽  
Large Scale ◽  
English Language ◽  
Management Practices ◽  
Smallholder Farmers ◽  
Management Strategies ◽  
Sub Saharan Africa ◽  
Smallholder Farming ◽  
Sub Saharan

Opportunities exist for adoption of precision agriculture technologies in all parts of the world. The form of precision agriculture may vary from region to region depending on technologies available, knowledge levels and mindsets. The current review examined research articles in the English language on precision agriculture practices for increased productivity among smallholder farmers in Sub-Saharan Africa. A total of 7715 articles were retrieved and after screening 128 were reviewed. The results indicate that a number of precision agriculture technologies have been tested under SSA conditions and show promising results. The most promising precision agriculture technologies identified were the use of soil and plant sensors for nutrient and water management, as well as use of satellite imagery, GIS and crop-soil simulation models for site-specific management. These technologies have been shown to be crucial in attainment of appropriate management strategies in terms of efficiency and effectiveness of resource use in SSA. These technologies are important in supporting sustainable agricultural development. Most of these technologies are, however, at the experimental stage, with only South Africa having applied them mainly in large-scale commercial farms. It is concluded that increased precision in input and management practices among SSA smallholder farmers can significantly improve productivity even without extra use of inputs.

scholarly journals A blueprint for developing and applying precision livestock farming tools: A key output of the EU-PLF project

2017 ◽  
Vol 7 (1) ◽  
pp. 12-17 ◽  
Author(s):  
Marcella Guarino ◽  
Tomas Norton ◽  
Dries Berckmans ◽  
Erik Vranken ◽  
Daniel Berckmans
Keyword(s):  
Livestock Farming ◽  
Precision Livestock Farming ◽  
The Eu

scholarly journals Planting Systems for Modern Olive Growing: Strengths and Weaknesses

Agriculture ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 494
Author(s):  
Riccardo Lo Bianco ◽  
Primo Proietti ◽  
Luca Regni ◽  
Tiziano Caruso
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Production Costs ◽  
Biotic And Abiotic Stress ◽  
Short Term ◽  
Mediterranean Countries ◽  
Social Concerns ◽  
The World ◽  
Fruit Growing ◽  
Cultivar Diversity

The objective of fully mechanizing olive harvesting has been pursued since the 1970s to cope with labor shortages and increasing production costs. Only in the last twenty years, after adopting super-intensive planting systems and developing appropriate straddle machines, a solution seems to have been found. The spread of super-intensive plantings, however, raises serious environmental and social concerns, mainly because of the small number of cultivars that are currently used (basically 2), compared to over 100 cultivars today cultivated on a large scale across the world. Olive growing, indeed, insists on over 11 million hectares. Despite its being located mostly in the Mediterranean countries, the numerous olive growing districts are characterized by deep differences in climate and soil and in the frequency and nature of environmental stress. To date, the olive has coped with biotic and abiotic stress thanks to the great cultivar diversity. Pending that new technologies supporting plant breeding will provide a wider number of cultivars suitable for super-intensive systems, in the short term, new growing models must be developed. New olive orchards will need to exploit cultivars currently present in various olive-growing areas and favor increasing productions that are environmentally, socially, and economically sustainable. As in fruit growing, we should focus on “pedestrian olive orchards”, based on trees with small canopies and whose top can be easily reached by people from the ground and by machines (from the side of the top) that can carry out, in a targeted way, pesticide treatments, pruning and harvesting.

scholarly journals Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6427
Author(s):  
Haoyu Niu ◽  
Derek Hollenbeck ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Large Scale ◽  
Bowen Ratio ◽  
Future Research ◽  
Accurate Estimation ◽  
Research Areas ◽  
Energy Balance Approach

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

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